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Remote Sensing
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Remote Sensing and Geoinformatics in Sustainable Development
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Remote Sensing and Geoinformatics in Sustainable Development

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing for Geospatial Science".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 2158

Special Issue Editors

Dr. Vyron Antoniou

E-Mail Website
Guest Editor
Hellenic Military Academy, Sector of Analysis and Theory of War, Athens, Greece
Interests: GIS; volunteer geographic information (VGI); remote sensing; spatial analysis; GIS-based modeling; cartography; applied geography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andreas Tsatsaris

E-Mail Website
Guest Editor
Department of Surveying and Geoinformatics Engineering, University of West Attica, 28 Ag. Spiridonos, Egaleo, 12243 Athens, Greece
Interests: GIS; cartography; medical geography; environmental health; spatial epidemiology; spatial archaeology
Special Issues, Collections and Topics in MDPI journals
Dr. Kleomenis Kalogeropoulos

E-Mail Website
Guest Editor
Department of Surveying and Geoinformatics Engineering, University of West Attica, 28 Ag. Spiridonos, Egaleo, 12243 Athens, Greece
Interests: geographic information systems (GIS); spatial data infrastructures (SDI); spatial analysis; cartography; human geography; physical geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing, with its remarkable capacity to capture vast volumes of data, has become the main tool in our global efforts to monitor and manage our planet's resources, ecosystems, and anthropogenic activities. When coupled with the analytical power of geoinformatics, this synergy empowers us to make well-informed decisions, shape policies, and execute strategies aimed at creating a sustainable future. Remote sensing and geoinformatics are powerful tools that have revolutionized our ability to monitor and manage our planet's resources and ecosystems. In the context of sustainable development, these technologies play a pivotal role in making informed decisions for a better and more sustainable future.

Remote sensing data, combined with geospatial analyses, offer valuable insights into the dynamics of several subjects affecting sustainability such as urban growth, agriculture, land use and land cover, natural resources, biodiversity, ecosystems and natural habitats, water resources, climate change, transportation and infrastructure development, disaster resilience and many more.

The scope of this Special Issue extends across a broad spectrum of disciplines, inviting exploration into the ways in which remote sensing and geoinformatics contribute to sustainable development. The aim is to explore the dynamic intertwining between these cutting-edge technologies and the imperative goal of sustainable progress. Thus, we encourage interdisciplinary collaborations and innovative approaches that advance our understanding and the practical application of these technologies in promoting a more sustainable and resilient future.

Dr. Vyron Antoniou
Prof. Dr. Andreas Tsatsaris
Dr. Kleomenis Kalogeropoulos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • geospatial analysis
  • sustainable development goals
  • earth observation
  • resilience

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

28 pages, 12784 KiB  
Article
Nonlinear Interactions and Dynamic Analysis of Ecosystem Resilience and Human Activities in China’s Potential Urban Agglomerations
by Xinyu Wang, Shidong Ge, Yaqiong Xu, László Kollányi and Tian Bai
Remote Sens. 2025, 17(11), 1955; https://doi.org/10.3390/rs17111955 - 5 Jun 2025
Viewed by 141
Abstract
Understanding the nonlinear relationship between human activity intensity (HAI) and ecosystem resilience (ER) is crucial for sustainability, yet underdeveloped areas are often overlooked. This study examines the Xuzhou Urban Agglomeration (XZUA) from 2012 to 2022, creating a framework to assess both ER and [...] Read more.
Understanding the nonlinear relationship between human activity intensity (HAI) and ecosystem resilience (ER) is crucial for sustainability, yet underdeveloped areas are often overlooked. This study examines the Xuzhou Urban Agglomeration (XZUA) from 2012 to 2022, creating a framework to assess both ER and HAI. Both frameworks utilize multi-source datasets, such as remote sensing, statistical yearbooks, and geospatial data. The ER framework uniquely combines dynamic and static indicators, while the HAI framework differentiates explicit and implicit human activity dimensions. We used spatial analysis, the Optimal Parameter Geodetector (OPGD), and Multi-Scale Geographically Weighted Regression (MGWR) to examine the nonlinear spatiotemporal interaction between HAI and ER. Results show the following: (1) ER exhibited a “shock-recovery” pattern with a net decline of 3.202%, while HAI followed a nonlinear “rise-fall” trend with a net decrease of 0.800%. (2) Spatial mismatches between HAI and ER intensified over time. (3) The negative correlation in high-HAI regions remained stable, whereas neighboring low-HAI areas deteriorated, indicating a spillover effect. (4) OPGD identified the change in HAI (Sen’s slope) as the primary driver of ER change (q = 0.512), with the strongest interaction observed between HAI Sen’s slope and precipitation (q = 0.802). (5) Compared to HAI intensity (mean), its temporal variation had a more spatially stable influence on ER. These findings offer insights for ecological management and sustainable planning in underdeveloped regions, highlighting the need for targeted HAI and ER interventions. Full article
(This article belongs to the Special Issue Remote Sensing and Geoinformatics in Sustainable Development)
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17 pages, 23633 KiB  
Article
Spatial and Temporal Dynamics of Transportation Accessibility in China: Insights from Sustainable Development Goal Indicators from 2015 to 2022
by Minshu Yang, Zhongchang Sun, Xiaoying Ouyang, Hongwei Li, Youmei Han and Dinoo Gunasekera
Remote Sens. 2024, 16(23), 4452; https://doi.org/10.3390/rs16234452 - 27 Nov 2024
Cited by 1 | Viewed by 1030
Abstract
SDG 9.1.1 and SDG 11.2.1 are significant evaluation indicators of the United Nations Sustainable Development Goals related to transportation accessibility and are used to measure the proportion of the population facilitating the use of road services in rural areas and the proportion of [...] Read more.
SDG 9.1.1 and SDG 11.2.1 are significant evaluation indicators of the United Nations Sustainable Development Goals related to transportation accessibility and are used to measure the proportion of the population facilitating the use of road services in rural areas and the proportion of the population facilitating the use of public transportation services in urban areas, respectively. However, there are currently challenges related to incomplete data and the inadequate interpretation of the indicators. In this study, we therefore evaluate the spatiotemporal patterns of the indicators and the number of disadvantaged groups in 337 Chinese cities from 2015 to 2022 based on multi-source data, and explore the spatial aggregation of the indicators and the driving factors. The results demonstrate that the indicator values of SDG 9.1.1 and SDG 11.2.1 reached 99.36% and 90.00%, respectively, in 2022, and the number of vulnerable groups decreased to approximately 1.89 million and 2.82 million. The indicator values of SDG 9.1.1 are high in the eastern part of China and low in the western part of the country, whereas the indicator values of SDG 11.2.1 exhibit spatial agglomeration in regions such as the Pearl River Delta. The average rural elevation and the density of urban public transportation stops are the most influential factors for these two indicators, respectively. The insights and data from this study provide support for improving transportation infrastructure and inequality in China, contributing to the achievement of the 2030 Sustainable Development Goals. Full article
(This article belongs to the Special Issue Remote Sensing and Geoinformatics in Sustainable Development)
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